A variety of new and advanced techniques have emerged in industrial process control, machine control, system surveillance, and condition based monitoring to address drawbacks of traditional sensor-threshold-based control and alarms. The traditional techniques did little more than provide responses to gross changes in individual metrics of a process or machine, often failing to provide adequate warning to prevent unexpected shutdowns, equipment damage, loss of product quality or catastrophic safety hazards.
According to one branch of the new techniques, empirical models of the monitored process or machine are used in both failure detection and control. Such models effectively leverage an aggregate view of surveillance sensor data to achieve much earlier incipient failure detection and finer process control. By modeling the many sensors on a process or machine simultaneously and in view of one another, the surveillance system can provide more information about how each sensor (and its measured parameter) ought to behave. Additionally, these approaches have the advantage that no additional instrumentation is typically needed, and sensors in place on the process or machine can be used.
An example of such an empirical surveillance system is described in U.S. Pat. No. 5,764,509 to Gross et al., the teachings of which are incorporated herein by reference in its entirety. In Gross, there is described an empirical model using a similarity operator against a reference library of known states of the monitored process, and an estimation engine for generating estimates of current process states based on the similarity operation, coupled with a sensitive statistical hypothesis test to determine if the current process state is a normal or abnormal state. Other empirical model-based monitoring systems known in the art employ neural networks to model the process or machine being monitored.
The role of the similarity operator in the above empirical surveillance system is to determine a metric of the similarity of a current set of sensor readings to any of the snapshots of sensor readings contained in the reference library. The similarity metric thusly rendered is used to generate an estimate of what the sensor readings ought to be, from a weighted composite of the reference library snapshots. The estimate can then be compared to the current readings for monitoring differences indicating incipient process upset, sensor failure or the like.
A variety of similarity operators are known, and generally render a metric between zero (signifying that the operators are not similar) and one (where the operators are identical) for comparisons of sensor values. Rendering a value in this range is useful for subsequently computing an estimate of the expected state of the monitored machine or process as a composite of known states. For example, a BART similarity operator is disclosed in U.S. Pat. No. 5,987,399 to Wegerich et al., the teachings of which are incorporated herein by reference in its entirety. Many such operators provide a discontinuous estimate for sensor values when the current snapshot presents a sensor reading that is outside the range of values in the reference library for that sensor. According to some implementations, when such circumstances are encountered, the similarity is not so much computed as simply set to zero. However, this can disregard important information about the relative offset of the current actual sensor reading compared to those in the reference set. What is needed, therefore, is an improved similarity operator that can accommodate sensor readings outside of the modeled range, and provide meaningful similarity values and sensor estimates for these circumstances. Furthermore, such a similarity operator should generally provide for accurate and meaningful results.